Penerapan Algoritma C4.5 Dalam Memprediksi Kebutuhan Pembibitan Pohon

  • Helmida Br. Manik * Mail Universitas Budi Darma, Medan, Indonesia
  • (*) Corresponding Author
Keywords: Data Minig; Prediction; Needs; Tree Nurseries; C4.5 Algorithm


A tree nursery is a place that is managed and designed to produce tree seedlings that are raised in good conditions until these seedlings are ready for planting. These tree nurseries can be small-scale informal nurseries or large commercial enterprises. Nurseries vary in size, facilities (supply, equipment, supplies, etc.), type of seed produced, and operations. Nurseries also have significant differences in the quality and quantity of stock of planting material produced. However, the primary goal of all nurseries is to produce sufficient quantities of high quality seed to meet the needs of seedling users. Seed users include the nursery operators themselves, individuals, organizations, communities, farmer groups, government agencies, non-governmental organizations, companies, or private consumers. The problems that have been experienced so far at the North Sumatran Forestry Service to determine seedling are still ineffective because determining the annual tree nursery is still very difficult, so to overcome this problem, the C4.5 algorithm is applied in predicting nurseries at the North Sumatra Forestry Service because of predictions. is a process of systematically estimating something that is most likely to happen in the future based on past and present information that is owned, so that the error (difference between something that happened and the predicted result) can be minimized. Prediction does not have to provide a definite answer to events that will occur, but seeks to find answers as close as possible to what will happen. The C4.5 algorithm is an algorithm that is used to produce a decision tree that is able to classify an object. C4.5 represents concepts in the form of a decision tree. The rules generated by C4.5 have a hierarchical relationship like a tree (having roots, points, branches, and leaves). Some call the structure of the model generated by C4.5 a decision tree while others call it a rule tree. Where in the process of working on the C4.5 algorithm, it calculates the gain and entropy values ​​for the data attributes that have been presented by the previous database. From the classification process and the results obtained are described in the form of a decision tree and based on the decision tree, new information comes from the previous database in the form of rules or rules private consumers


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